[Submitted on 8 Apr 2026 (v1), last revised 12 Oct 2026 (this version, v2)]
Abstract:While decoupled control schemes for legged mobile manipulators have shown robustness, learning holistic whole-body control policies for tracking global end-effector poses remains fragile against Out-of-Distribution (OOD) inputs induced by sensor noise or infeasible user commands. To improve robustness against these perturbations without sacrificing task performance and continuity, we propose Competence Manifold Projection (CMP). Specifically, we utilize a Frame-Wise Safety Scheme that transforms the infinite-horizon safety constraint into a computationally efficient single-step manifold inclusion. To instantiate this competence manifold, we employ a Lower-Bounded Safety Estimator that distinguishes unmastered intentions from the training distribution. We then introduce an Isomorphic Latent Space (ILS) that aligns manifold geometry with safety probability, enabling efficient O(1) seamless defense against arbitrary OOD intents. Experiments demonstrate that CMP achieves up to a 10-fold survival rate improvement in typical OOD scenarios where baselines suffer catastrophic failure, incurring under 10% tracking degradation. Notably, the system exhibits emergent “best-effort” generalization behaviors to progressively accomplish OOD goals by adhering to the competence boundaries. Project website: this https URL
Submission history
From: Ziyang Cheng [view email]
[v1] Wed, 8 Apr 2026 18:00:39 UTC (16,698 KB)
[v2] Mon, 12 Oct 2026 16:50:13 UTC (18,574 KB)
